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International Journal of Adolescent Medicine and Health

Editor-in-Chief: Merrick, Joav

Editorial Board Member: Birch, Diana ML / Blum, Robert W. / Greydanus, MD, Dr. HC (Athens), Donald E. / Hardoff, Daniel / Kerr, Mike / Levy, Howard B / Morad, Mohammed / Omar, Hatim A. / de Paul, Joaquin / Rydelius, Per-Anders / Shek, Daniel T.L. / Sher, Leo / Silber, Tomas J. / Towns, Susan / Urkin, Jacob / Verhofstadt-Deneve, Leni / Zeltzer, Lonnie / Tenenbaum, Ariel

SCImago Journal Rank (SJR) 2015: 0.346
Source Normalized Impact per Paper (SNIP) 2015: 0.310
Impact per Publication (IPP) 2015: 0.660

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Collaborative evaluation and management of students’ health-related physical fitness: applications of cluster analysis and the classification tree

Jou-An Chen1 / Chi-Chuan Shih2 / Pay-Fan Lin3 / Jin-Jong Chen4 / 3

1National Siluo Agriculturial Industrial High School, Yunlin, Taiwan

2National Nantou Commercial High School, Nantou, Taiwan

3School of Nursing, National Taipei University of Nursing and Health Science, Taipei, Taiwan

4Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, Taiwan

Corresponding author: Kuan-Chia Lin, PhD, National Taipei University of Nursing and Health Sciences, No. 365, Ming-te Road, Peitou District, Taipei, Taiwan

Citation Information: . Volume 24, Issue 2, Pages 153–159, ISSN (Online) 2191-0278, ISSN (Print) 0334-0139, DOI: https://doi.org/10.1515/ijamh.2012.023, November 2011

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Health-related physical fitness has decreased with age; this is od immense concern to adolescents. School-based health intervention programs can be classified as either population-wide or high-risk approach. Although the population-wide and risk-based approaches adopt different healthcare angles, they all need to focus resources on risk evaluation. In this paper, we describe an exploratory application of cluster analysis and the tree model to collaborative evaluation of students’ health- related physical fitness from a high school sample in Taiwan (n=742). Cluster analysis show that physical fitness can be divided into relatively good, moderate and poor subgroups. There are significant differences in biochemical measurements among these three groups. For the tree model, we used 2004 school-year students as an experimental group and 2005 school-year students as a validation group. The results indicate that if sit-and-reach is shorter than 33 cm, BMI is >25.46 kg/m2, and 1600 m run/walk is >534 s, the predicted probability for the number of metabolic risk factors ≥2 is 100% and the population is 41, both results are the highest. From the risk-based healthcare viewpoint, the cluster analysis can sort out students’ physical fitness data in a short time and then narrow down the scope to recognize the subgroups. A classification tree model specifically shows the discrimination paths between the measurements of physical fitness for metabolic risk and would be helpful for self-management or proper healthcare education targeting different groups. Applying both methods to specific adolescents’ health issues could provide different angles in planning health promotion projects.

Keywords: classification tree; cluster analysis; health-related physical fitness

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